with simulation time-step \(\mathrm {\Delta }t\)  and learning rate \(\eta \) , which
effectuates a pre-synaptic normalization according to Oja's rule [1]}.
Similarly, the updated feedforward weights \(\mathbf {W}^{r\prime }\)  are computed for
reward \(k\)  as
\( W^{r\,\prime }_{ik}=W^r_{ik}+\mathrm {\Delta }t\,\eta _r V^r_{ik}\,p_i(r_{ik}- \ p_iW^r_{ik}) \,.\) 
